RK3588 opencv maliGPU图像拼接

1 左边图

图像大小:1920*1080

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2右边图

图像大小:1920*1080
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3拼接好的图像

图像大小：1920 *1080
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4代码

#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/highgui.hpp>//图像融合
#include <opencv2/xfeatures2d.hpp>//拼接算法
#include <opencv2/calib3d.hpp>
#include <opencv2/imgproc.hpp>
#include"openclTool.h"using namespace std;
using namespace cv;
using namespace cv::xfeatures2d;typedef struct
{Point2f left_top;Point2f left_bottom;Point2f right_top;Point2f right_bottom;
}four_corners_t;four_corners_t corners;void CalcCorners(const Mat& H, const Mat& src)
{double v2[] = { 0, 0, 1 };//左上角double v1[3];//变换后的坐标值Mat V2 = Mat(3, 1, CV_64FC1, v2);  //列向量Mat V1 = Mat(3, 1, CV_64FC1, v1);  //列向量V1 = H * V2;//左上角(0,0,1)cout << "V2: " << V2 << endl;cout << "V1: " << V1 << endl;corners.left_top.x = v1[0] / v1[2];corners.left_top.y = v1[1] / v1[2];//左下角(0,src.rows,1)v2[0] = 0;v2[1] = src.rows;v2[2] = 1;V2 = Mat(3, 1, CV_64FC1, v2);  //列向量V1 = Mat(3, 1, CV_64FC1, v1);  //列向量V1 = H * V2;corners.left_bottom.x = v1[0] / v1[2];corners.left_bottom.y = v1[1] / v1[2];//右上角(src.cols,0,1)v2[0] = src.cols;v2[1] = 0;v2[2] = 1;V2 = Mat(3, 1, CV_64FC1, v2);  //列向量V1 = Mat(3, 1, CV_64FC1, v1);  //列向量V1 = H * V2;corners.right_top.x = v1[0] / v1[2];corners.right_top.y = v1[1] / v1[2];//右下角(src.cols,src.rows,1)v2[0] = src.cols;v2[1] = src.rows;v2[2] = 1;V2 = Mat(3, 1, CV_64FC1, v2);  //列向量V1 = Mat(3, 1, CV_64FC1, v1);  //列向量V1 = H * V2;corners.right_bottom.x = v1[0] / v1[2];corners.right_bottom.y = v1[1] / v1[2];}//图像融合的去裂缝处理操作
void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst)
{int start = MIN(corners.left_top.x, corners.left_bottom.x);//开始位置，即重叠区域的左边界double processWidth = img1.cols - start;//重叠区域的宽度int rows = dst.rows;int cols = img1.cols; //注意，是列数*通道数double alpha = 1;//img1中像素的权重for (int i = 0; i < rows; i++){uchar* p = img1.ptr<uchar>(i);  //获取第i行的首地址uchar* t = trans.ptr<uchar>(i);uchar* d = dst.ptr<uchar>(i);for (int j = start; j < cols; j++){//如果遇到图像trans中无像素的黑点，则完全拷贝img1中的数据if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0){alpha = 1;}else{//img1中像素的权重，与当前处理点距重叠区域左边界的距离成正比，实验证明，这种方法确实好alpha = (processWidth - (j - start)) / processWidth;}d[j * 3] = p[j * 3] * alpha + t[j * 3] * (1 - alpha);d[j * 3 + 1] = p[j * 3 + 1] * alpha + t[j * 3 + 1] * (1 - alpha);d[j * 3 + 2] = p[j * 3 + 2] * alpha + t[j * 3 + 2] * (1 - alpha);}}
}int run()
{//左图Mat left = imread("0519_1.jpg");//右图Mat right = imread("0519_2.jpg");resize(left, left, Size(1920,1080), 0, 0, INTER_LINEAR);resize(right, right, Size(1920,1080), 0, 0, INTER_LINEAR);//左右图显示imshow("left",left);imshow("right",right);imwrite("left.jpg",left);imwrite("right.jpg",right);//创建SURF对象Ptr<SURF> surf;//create 函数参数 海森矩阵阀值 800特征点以内surf = SURF::create(800);//创建一个暴力匹配器 用于特征点匹配BFMatcher matcher;//特征点容器 存放特征点KeyPointvector<KeyPoint>key1,key2;//保存特征点Mat c,d;//1、选择特征点//左图 右图 识别特征点 是Mat对象 用c d保存surf->detectAndCompute(left,Mat(),key2,d);surf->detectAndCompute(right,Mat(),key1,c);//特征点对比，保存   特征点为中心点区域比对vector<DMatch> matches;matcher.match(d,c,matches);//排序从小到大 找到特征点连线sort(matches.begin(),matches.end());//2、保存最优的特征点对象vector<DMatch>good_matches;int ptrpoint = std::min(50,(int)(matches.size()*0.15));for (int i = 0;i < ptrpoint;i++){good_matches.push_back(matches[i]);}//2-1、画线 最优的特征点对象连线Mat outimg;drawMatches(left,key2,right,key1,good_matches,outimg,Scalar::all(-1),Scalar::all(-1),vector<char>(),DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);//imshow("outimg",outimg);//3、特征点匹配vector<Point2f>imagepoint1,imagepoint2;for (int i= 0 ;i < good_matches.size();i++){//查找特征点可连接处                          变形imagepoint1.push_back(key1[good_matches[i].trainIdx].pt);//查找特征点可连接处                          查找基准线imagepoint2.push_back(key2[good_matches[i].queryIdx].pt);}//4、透视转换 图形融合Mat homo = findHomography(imagepoint1,imagepoint2,cv::RANSAC);//imshow("homo",homo);//根据透视转换矩阵进行计算 四个坐标CalcCorners(homo,right);//接收透视转换结果while (1){auto time0=time();Mat imageTransForm;//透视转换warpPerspective(right,imageTransForm,homo,Size(MAX(corners.right_top.x,corners.right_bottom.x),left.rows));//右图透视变换 由于本次图片材料是自己截图拼接的 因此看不出透视变换的明显特征//imshow("imageTransForm",imageTransForm);//结果进行整合int dst_width = imageTransForm.cols;int dst_height = left.rows;Mat dst(dst_height,dst_width,CV_8UC3);dst.setTo(0);imageTransForm.copyTo(dst(Rect(0,0,imageTransForm.cols,imageTransForm.rows)));left.copyTo(dst(Rect(0,0,left.cols,left.rows)));//5、优化图像OptimizeSeam(left,imageTransForm,dst);auto time1=time();auto time_use = time_diff(time0, time1);std::cout << "--=--]time cost-:" <<time_use <<std::endl;//最终图像拼接结果imshow("dst",dst);waitKey(1);}return 0;
}
int main()
{while(1)
{run();}  
}